Articles | Volume 28, issue 3
https://doi.org/10.5194/npg-28-423-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-423-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Sep 2021
Research article |
| 10 Sep 2021
| 10 Sep 2021
Enhancing geophysical flow machine learning performance via scale separation
Davide Faranda
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
London Mathematical Laboratory, 8 Margravine Gardens, London, W68RH, UK
LMD/IPSL, Ecole Normale Superieure, PSL research University, Paris, France
Mathieu Vrac
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Pascal Yiou
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Flavio Maria Emanuele Pons
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Adnane Hamid
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Giulia Carella
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Cedric Ngoungue Langue
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Soulivanh Thao
Laboratoire des Sciences du Climat et de l'Environnement, CE Saclay l'Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay & IPSL, 91191 Gif-sur-Yvette, France
Valerie Gautard
DRF/IRFU/DEDIP//LILAS
Departement d'Electronique des Detecteurs et d'Informatique pour la Physique, CE Saclay l'Orme des Merisiers, 91191 Gif-sur-Yvette, France.
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Atmospheric jets are a key component of the climate system and of our everyday lives. Indeed, they affect human activities by influencing the weather in many mid-latitude regions. However, we still lack a complete understanding of their dynamical properties. In this study, we try to relate the understanding gained in idealized computer simulations of the jets to our knowledge from observations of the real atmosphere.
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The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
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Ferran Lopez-Marti, Mireia Ginesta, Davide Faranda, Anna Rutgersson, Pascal Yiou, Lichuan Wu, and Gabriele Messori
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Cedric Gacial Ngoungue Langue, Helene Brogniez, and Philippe Naveau
EGUsphere, https://doi.org/10.5194/egusphere-2024-3481, https://doi.org/10.5194/egusphere-2024-3481, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Cedric G. Ngoungue Langue, Christophe Lavaysse, and Cyrille Flamant
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Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
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Flavio Maria Emanuele Pons and Davide Faranda
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Short summary
Short summary
The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Miriam D'Errico, Flavio Pons, Pascal Yiou, Soulivanh Tao, Cesare Nardini, Frank Lunkeit, and Davide Faranda
Earth Syst. Dynam., 13, 961–992, https://doi.org/10.5194/esd-13-961-2022, https://doi.org/10.5194/esd-13-961-2022, 2022
Short summary
Short summary
Climate change is already affecting weather extremes. In a warming climate, we will expect the cold spells to decrease in frequency and intensity. Our analysis shows that the frequency of circulation patterns leading to snowy cold-spell events over Italy will not decrease under business-as-usual emission scenarios, although the associated events may not lead to cold conditions in the warmer scenarios.
Bérengère Dubrulle, François Daviaud, Davide Faranda, Louis Marié, and Brice Saint-Michel
Nonlin. Processes Geophys., 29, 17–35, https://doi.org/10.5194/npg-29-17-2022, https://doi.org/10.5194/npg-29-17-2022, 2022
Short summary
Short summary
Present climate models discuss climate change but show no sign of bifurcation in the future. Is this because there is none or because they are in essence too simplified to be able to capture them? To get elements of an answer, we ran a laboratory experiment and discovered that the answer is not so simple.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
Short summary
Short summary
This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Gabriele Messori and Davide Faranda
Clim. Past, 17, 545–563, https://doi.org/10.5194/cp-17-545-2021, https://doi.org/10.5194/cp-17-545-2021, 2021
Short summary
Short summary
The palaeoclimate community must both analyse large amounts of model data and compare very different climates. Here, we present a seemingly very abstract analysis approach that may be fruitfully applied to palaeoclimate numerical simulations. This approach characterises the dynamics of a given climate through a small number of metrics and is thus suited to face the above challenges.
Gabriele Messori, Nili Harnik, Erica Madonna, Orli Lachmy, and Davide Faranda
Earth Syst. Dynam., 12, 233–251, https://doi.org/10.5194/esd-12-233-2021, https://doi.org/10.5194/esd-12-233-2021, 2021
Short summary
Short summary
Atmospheric jets are a key component of the climate system and of our everyday lives. Indeed, they affect human activities by influencing the weather in many mid-latitude regions. However, we still lack a complete understanding of their dynamical properties. In this study, we try to relate the understanding gained in idealized computer simulations of the jets to our knowledge from observations of the real atmosphere.
Flavio Maria Emanuele Pons and Davide Faranda
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-352, https://doi.org/10.5194/nhess-2020-352, 2020
Preprint withdrawn
Short summary
Short summary
The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
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Short summary
Machine learning approaches are spreading rapidly in climate sciences. They are of great help in many practical situations where using the underlying equations is difficult because of the limitation in computational power. Here we use a systematic approach to investigate the limitations of the popular echo state network algorithms used to forecast the long-term behaviour of chaotic systems, such as the weather. Our results show that noise and intermittency greatly affect the performances.
Machine learning approaches are spreading rapidly in climate sciences. They are of great help in...
